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<a href="_c_pam_impl_8cs.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 ﻿using System;
<a name="l00002"></a>00002 <span class="keyword">using</span> System.Collections.Generic;
<a name="l00003"></a>00003 <span class="keyword">using</span> System.Linq;
<a name="l00004"></a>00004 <span class="keyword">using</span> System.Text;
<a name="l00005"></a>00005 
<a name="l00006"></a>00006 <span class="keyword">using</span> ClusterAggregation.Datum;
<a name="l00007"></a>00007 <span class="keyword">using</span> ClusterAggregation.DataSets;
<a name="l00008"></a>00008 
<a name="l00009"></a>00009 <span class="keyword">namespace </span>ClusterAggregation.Clusterers
<a name="l00010"></a>00010 {
<a name="l00015"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html">00015</a>         <span class="keyword">public</span> <span class="keyword">class </span><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html">CPamImpl</a>:<a class="code" href="interface_cluster_aggregation_1_1_clusterers_1_1_i_clusterer.html">IClusterer</a>
<a name="l00016"></a>00016         {
<a name="l00018"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#a5dbbdedeb78f0081bf4a314c4c7cd0b4">00018</a>                 <span class="keyword">protected</span> <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#a5dbbdedeb78f0081bf4a314c4c7cd0b4">m_distFunc</a>;
<a name="l00020"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#aba63940775deb6459bd2c3c823196856">00020</a>                 <span class="keyword">protected</span> Random <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#aba63940775deb6459bd2c3c823196856">m_randomGenerator</a>;
<a name="l00022"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#ab5b00b7444068774e8b34863e7c10a14">00022</a>                 <span class="keyword">protected</span> <span class="keywordtype">int</span> m_repeats = 1;
<a name="l00024"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#a19ea9d5b4d1979694985c41965ffc2cc">00024</a>                 <span class="keyword">protected</span> <span class="keywordtype">int</span> m_iterLimit = 100;
<a name="l00025"></a>00025 
<a name="l00032"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#a5b0eb597f96318080a092163fe6923b9">00032</a>                 <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html">CPamImpl</a>(<a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> dm = null, Random rand = null)
<a name="l00033"></a>00033                 {
<a name="l00034"></a>00034                         this.m_distFunc = dm;
<a name="l00035"></a>00035                         <span class="keywordflow">if</span> (dm == null)
<a name="l00036"></a>00036                                 this.m_distFunc = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_datum_1_1_c_euclidean_distance_similarity_function.html">CEuclideanDistanceSimilarityFunction</a>();
<a name="l00037"></a>00037                         this.m_randomGenerator = rand;
<a name="l00038"></a>00038                         <span class="keywordflow">if</span> (rand == null)
<a name="l00039"></a>00039                                 this.m_randomGenerator = <span class="keyword">new</span> Random();
<a name="l00040"></a>00040                 }
<a name="l00041"></a>00041         
<a name="l00050"></a><a class="code" href="class_cluster_aggregation_1_1_clusterers_1_1_c_pam_impl.html#abc890f6d044745ffd5555f51eb25d9e9">00050</a>                 <span class="keyword">public</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a> cluster(<a class="code" href="class_cluster_aggregation_1_1_datum_1_1_a_data.html">AData</a>[] data, <a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html">ISimilarity</a> dm,  <span class="keywordtype">int</span> numOfClusters)
<a name="l00051"></a>00051                 {
<a name="l00052"></a>00052                         <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a> res = <span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html">CPartition</a>();
<a name="l00053"></a>00053                         res.<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#a1be9e2847752c965f383b344318065b7">name</a> = <span class="stringliteral">&quot;PAM&quot;</span>;
<a name="l00054"></a>00054                         <span class="keywordtype">int</span>[] assignments = <span class="keyword">new</span> <span class="keywordtype">int</span>[data.Length];
<a name="l00055"></a>00055                         <span class="keywordtype">int</span>[] medioids = <span class="keyword">new</span> <span class="keywordtype">int</span>[numOfClusters];
<a name="l00056"></a>00056                         <span class="keywordtype">double</span> totalDistance = 0;
<a name="l00057"></a>00057                         <span class="keywordtype">int</span> changes = -1;
<a name="l00058"></a>00058                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; assignments.Length; i++)
<a name="l00059"></a>00059                         {
<a name="l00060"></a>00060                                 assignments[i] = -1;
<a name="l00061"></a>00061                         }<span class="comment">//-1, invalid category!</span>
<a name="l00062"></a>00062                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; numOfClusters; i++)
<a name="l00063"></a>00063                         {
<a name="l00064"></a>00064                                 res.<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#a749d3edca2897d2127ce95934648d3ea">clusters</a>.Add(<span class="keyword">new</span> <a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_cluster.html">CCluster</a>());
<a name="l00065"></a>00065                         }
<a name="l00066"></a>00066                         <span class="keywordtype">int</span>[] bestMedCand = <span class="keyword">new</span> <span class="keywordtype">int</span>[medioids.Length];
<a name="l00067"></a>00067                         <span class="keywordtype">double</span>[] bestMedCandDist = <span class="keyword">new</span> <span class="keywordtype">double</span>[medioids.Length];            
<a name="l00068"></a>00068 
<a name="l00069"></a>00069                         <span class="keywordtype">int</span> iter = 0;
<a name="l00070"></a>00070                         <span class="keywordflow">do</span>
<a name="l00071"></a>00071                         {
<a name="l00072"></a>00072                                 changes = 0;
<a name="l00073"></a>00073                                 totalDistance = 0.0;
<a name="l00074"></a>00074 
<a name="l00075"></a>00075                                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; data.Length; i++)
<a name="l00076"></a>00076                                 {                    
<a name="l00077"></a>00077                                         <span class="keywordtype">int</span> assignment = 0;
<a name="l00078"></a>00078                                         <span class="keywordtype">double</span> minDist = dm.<a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html#a41a7305544a32ec0abbb5b6be73ce23a">similarity</a>(data[medioids[0]], data[i]);
<a name="l00079"></a>00079 
<a name="l00080"></a>00080                                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> k = 1; k &lt; medioids.Length; k++)
<a name="l00081"></a>00081                                         {
<a name="l00082"></a>00082                                                 <span class="keywordtype">double</span> dist = dm.<a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html#a41a7305544a32ec0abbb5b6be73ce23a">similarity</a>(data[medioids[k]], data[i]);
<a name="l00083"></a>00083                                                 <span class="keywordflow">if</span> (dist &lt; minDist)
<a name="l00084"></a>00084                                                 {
<a name="l00085"></a>00085                                                         minDist = dist;
<a name="l00086"></a>00086                                                         assignment = k;
<a name="l00087"></a>00087                                                 }
<a name="l00088"></a>00088                                         }
<a name="l00089"></a>00089 
<a name="l00090"></a>00090                                         <span class="comment">//Update which cluster it is in</span>
<a name="l00091"></a>00091                                         <span class="keywordflow">if</span> (assignments[i] != assignment)
<a name="l00092"></a>00092                                         {
<a name="l00093"></a>00093                                                 changes++;
<a name="l00094"></a>00094                                                 assignments[i] = assignment;                        
<a name="l00095"></a>00095                                         }
<a name="l00096"></a>00096                                         totalDistance += minDist * minDist;
<a name="l00097"></a>00097 
<a name="l00098"></a>00098                                 }
<a name="l00099"></a>00099 
<a name="l00100"></a>00100 
<a name="l00101"></a>00101                                 <span class="comment">//Update the medioids               </span>
<a name="l00102"></a>00102                                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; bestMedCandDist.Length; i++)
<a name="l00103"></a>00103                                 {
<a name="l00104"></a>00104                                         bestMedCandDist[i] = <span class="keywordtype">double</span>.MaxValue;
<a name="l00105"></a>00105                                 }
<a name="l00106"></a>00106                                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; data.Length; i++)
<a name="l00107"></a>00107                                 {
<a name="l00108"></a>00108                                         <span class="keywordtype">double</span> thisCandidateDistance = 0.0;
<a name="l00109"></a>00109                                         <span class="keywordtype">int</span> clusterID = assignments[i];                   
<a name="l00110"></a>00110                                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; data.Length; j++)
<a name="l00111"></a>00111                                         {
<a name="l00112"></a>00112                                                 <span class="keywordflow">if</span> (j == i || assignments[j] != clusterID)
<a name="l00113"></a>00113                                                         <span class="keywordflow">continue</span>;
<a name="l00114"></a>00114                                                 thisCandidateDistance += Math.Pow(dm.<a class="code" href="interface_cluster_aggregation_1_1_datum_1_1_i_similarity.html#a41a7305544a32ec0abbb5b6be73ce23a">similarity</a>(data[i], data[j]), 2);
<a name="l00115"></a>00115                                         }
<a name="l00116"></a>00116 
<a name="l00117"></a>00117                                         <span class="keywordflow">if</span> (thisCandidateDistance &lt; bestMedCandDist[clusterID])
<a name="l00118"></a>00118                                         {
<a name="l00119"></a>00119                                                 bestMedCand[clusterID] = i;
<a name="l00120"></a>00120                                                 bestMedCandDist[clusterID] = thisCandidateDistance;
<a name="l00121"></a>00121                                         }
<a name="l00122"></a>00122                                 }
<a name="l00123"></a>00123                                 <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; medioids.Length; i++)
<a name="l00124"></a>00124                                 {
<a name="l00125"></a>00125                                                 medioids[i] = bestMedCand[i];
<a name="l00126"></a>00126                                 }                
<a name="l00127"></a>00127                         }
<a name="l00128"></a>00128                         <span class="keywordflow">while</span> (changes &gt; 0 &amp;&amp; iter++ &lt; m_iterLimit);
<a name="l00129"></a>00129 
<a name="l00130"></a>00130                         <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; data.Length; i++)
<a name="l00131"></a>00131                         {
<a name="l00132"></a>00132                                 res.<a class="code" href="class_cluster_aggregation_1_1_data_sets_1_1_c_partition.html#a749d3edca2897d2127ce95934648d3ea">clusters</a>[assignments[i]].data.Add(data[i]);
<a name="l00133"></a>00133                         }
<a name="l00134"></a>00134                         <span class="keywordflow">return</span> res;
<a name="l00135"></a>00135                 }
<a name="l00136"></a>00136         }
<a name="l00137"></a>00137 }
<a name="l00138"></a>00138 
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